Generative AI in Insurance: $50-$70b Is on the Table
Generative AI isn't a side project anymore. A new report indicates it could unlock $50b to $70b in additional revenue for the insurance industry, with most gains landing in marketing and sales, customer operations, and software engineering.
Private equity is still active across insurance-even after a softer 2025-because the fundamentals look strong for firms that can adopt and scale AI. Here's where the money shows up, and how to capture it.
Deal Flow Snapshot: Where Capital Is Moving
- Brokers remain the bulk of activity (about 70% of deals) but saw ~20% YoY decline as the market matured.
- MGAs represent roughly 5% of deal flow; TPAs recorded ~15% average annual deal growth over the past five years.
- Software and data providers held up thanks to recurring revenue profiles.
- US momentum: private equity investment grew 26% per year from 2020 to 2025; H1 2025 hit $6.3b across 164 deals.
- Europe saw invested capital decline ~18% annually between 2020 and H1 2025.
Where GenAI Drives Revenue (Near Term)
- Marketing and Sales: Predictive lead scoring, dynamic cross-sell/upsell, hyper-personalized outreach, proposal drafting. Expect higher conversion, lower CAC, and faster cycle times.
- Customer Operations: AI-assisted FNOL intake, claims triage, document summarization, agent copilot in contact centers, and self-serve policy changes. Expect shorter handle times and better CSAT.
- Software Engineering: Code generation, test automation, and faster remediation. Expect shorter release cycles and fewer defects.
Speed Wins: Underwriting and Quoting
AI is compressing underwriting and quoting from weeks to days-and in some cases from two to three days down to one to two hours. That time back translates to more quotes, higher bind rates, and sharper responsiveness for brokers and MGAs.
The MGA Moment
MGAs have been one of the fastest-growing segments. US premiums climbed from $47b in 2020 to $97b in 2024 (about 14% CAGR). With AI streamlining intake, enrichment, and straight-through processing, MGAs can scale distribution without ballooning headcount.
What This Means for Your Firm
- Carriers: Modernize submissions intake, appetite checks, and pricing support; deploy underwriter copilots; tighten claims leakage controls with AI-driven review and subrogation signals.
- Brokers: Use AI to prioritize pipelines, auto-draft proposals, and accelerate remarketing. Standardize intake to cut friction for carriers and MGAs.
- MGAs: Push straight-through binding for smaller risks; implement document AI for bordereaux and endorsements; build a data flywheel to improve placement and loss ratio.
- TPAs: Embed triage models, automate adjudication steps, and provide analytics portals that prove turnaround and accuracy to carrier clients.
A Practical 90-Day Plan
- Pick 2-3 needle-movers: Example stack: submission ingestion, agent/copilot for service, and code-assist for engineering.
- Stand up secure foundations: Data access controls, PII handling, model governance, and red-teaming for prompt/output risks.
- Pilot with clear baselines: Time-to-quote, handle time, conversion, bind rate, claim cycle time, and defect rates.
- Train the front line: Short playbooks, prompt libraries, and escalation guidelines. Reward adoption, not just outcomes.
- Measure weekly, scale monthly: Kill what doesn't move a KPI by ≥10-20%; double down on winners.
Metrics That Matter
- Growth: Lead-to-quote conversion, quote-to-bind, upsell rate, marketing CAC.
- Speed and Cost: Time-to-quote, average handle time, cost per claim, time to resolution, engineering cycle time.
- Quality and Risk: Loss ratio signals, leakage, error rates, compliance flags, customer satisfaction.
Build the Stack (Without Overbuilding)
- Data: Clean submission and policy data, document pipelines, and feedback loops from outcomes back to models.
- Models: Mix general LLMs with insurance-tuned components for classification, extraction, and recommendations.
- Workflow: Put AI inside existing systems (policy admin, CRM, claims) so users don't context-switch.
- Controls: Human-in-the-loop for pricing, coverage, and denials; clear audit trails for every AI-assisted decision.
AI will reshape current insurance models more than it replaces them. Returns will depend on how effectively you adopt and scale-process design, data feedback, and talent enablement are the real moats.
Go Deeper
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